Radiological Physics and Technology最新文献_第3页

Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning. 自我监督学习中基于乳腺组织结构的拼图任务的乳腺癌分类。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-06 DOI: 10.1007/s12194-024-00874-y

Keisuke Sugawara, Eichi Takaya, Ryusei Inamori, Yuma Konaka, Jumpei Sato, Yuta Shiratori, Fumihito Hario, Tomoya Kobayashi, Takuya Ueda, Yoshikazu Okamoto

{"title":"Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning.","authors":"Keisuke Sugawara, Eichi Takaya, Ryusei Inamori, Yuma Konaka, Jumpei Sato, Yuta Shiratori, Fumihito Hario, Tomoya Kobayashi, Takuya Ueda, Yoshikazu Okamoto","doi":"10.1007/s12194-024-00874-y","DOIUrl":"10.1007/s12194-024-00874-y","url":null,"abstract":"Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task in SSL enables models to learn both features of images and the positional relationships within images. In breast cancer diagnosis, radiologists evaluate not only lesion-specific features but also the surrounding breast structures. However, deep learning models that adopt a diagnostic approach similar to human radiologists are still limited. This study aims to evaluate the effectiveness of the Jigsaw puzzle task in characterizing breast tissue structures for breast cancer classification on mammographic images. Using the Chinese Mammography Database (CMMD), we compared four pre-training pipelines: (1) IN-Jig, pre-trained with both the ImageNet classification task and the Jigsaw puzzle task, (2) Scratch-Jig, pre-trained only with the Jigsaw puzzle task, (3) IN, pre-trained only with the ImageNet classification task, and (4) Scratch, that is trained from random initialization without any pre-training tasks. All pipelines were fine-tuned using binary classification to distinguish between the presence or absence of breast cancer. Performance was evaluated based on the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Additionally, detailed analysis was conducted for performance across different radiological findings, breast density, and regions of interest were visualized using gradient-weighted class activation mapping (Grad-CAM). The AUC for the four models were 0.925, 0.921, 0.918, 0.909, respectively. Our results suggest the Jigsaw puzzle task is an effective pre-training method for breast cancer classification, with the potential to enhance diagnostic accuracy with limited data.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"209-218"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images. 在 T1 加权脑磁共振图像的超分辨率处理中使用多重对比图像进行数据集扩增。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-03-01 Epub Date: 2024-12-16 DOI: 10.1007/s12194-024-00871-1

Hajime Kageyama, Nobukiyo Yoshida, Keisuke Kondo, Hiroyuki Akai

{"title":"Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images.","authors":"Hajime Kageyama, Nobukiyo Yoshida, Keisuke Kondo, Hiroyuki Akai","doi":"10.1007/s12194-024-00871-1","DOIUrl":"10.1007/s12194-024-00871-1","url":null,"abstract":"This study investigated the effectiveness of augmenting datasets for super-resolution processing of brain Magnetic Resonance Images (MRI) T1-weighted images (T1WIs) using deep learning. By incorporating images with different contrasts from the same subject, this study sought to improve network performance and assess its impact on image quality metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). This retrospective study included 240 patients who underwent brain MRI. Two types of datasets were created: the Pure-Dataset group comprising T1WIs and the Mixed-Dataset group comprising T1WIs, T2-weighted images, and fluid-attenuated inversion recovery images. A U-Net-based network and an Enhanced Deep Super-Resolution network (EDSR) were trained on these datasets. Objective image quality analysis was performed using PSNR and SSIM. Statistical analyses, including paired t test and Pearson's correlation coefficient, were conducted to evaluate the results. Augmenting datasets with images of different contrasts significantly improved training accuracy as the dataset size increased. PSNR values ranged 29.84-30.26 dB for U-Net trained on mixed datasets, and SSIM values ranged 0.9858-0.9868. Similarly, PSNR values ranged 32.34-32.64 dB for EDSR trained on mixed datasets, and SSIM values ranged 0.9941-0.9945. Significant differences in PSNR and SSIM were observed between models trained on pure and mixed datasets. Pearson's correlation coefficient indicated a strong positive correlation between dataset size and image quality metrics. Using diverse image data obtained from the same subject can improve the performance of deep-learning models in medical image super-resolution tasks.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"172-185"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Water/fat separate reconstruction for body quantitative susceptibility mapping in MRI. 水/脂肪分离重建在MRI中用于身体定量敏感性制图。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-06 DOI: 10.1007/s12194-024-00878-8

Hirohito Kan, Masahiro Nakashima, Takahiro Tsuchiya, Masato Yamada, Akio Hiwatashi

{"title":"Water/fat separate reconstruction for body quantitative susceptibility mapping in MRI.","authors":"Hirohito Kan, Masahiro Nakashima, Takahiro Tsuchiya, Masato Yamada, Akio Hiwatashi","doi":"10.1007/s12194-024-00878-8","DOIUrl":"10.1007/s12194-024-00878-8","url":null,"abstract":"This study aimed to investigate the cause of susceptibility underestimation in body quantitative susceptibility mapping (QSM) and propose a water/fat separate reconstruction to address this issue. A numerical simulation was conducted using conventional QSM with/without body masking. The conventional method with body masking underestimated the susceptibility across all regions, whereas the method without body masking estimated an equivalent value to the ground truth. Additional numerical simulations and human experiments were conducted to compare the water/fat separate reconstruction, which separately reconstructs water and fat susceptibility maps based on the water/fat separation, with conventional QSM with body masking. The proposed method improved susceptibility estimation specifically in only the water tissue. The results of the human experiments were consistent with those of the numerical simulations. The lack of phase information outside the body contributed to susceptibility underestimation in conventional QSM. The developed method addressed susceptibility underestimation only in water tissue in body QSM.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"320-328"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of gravity effect on liver and spleen volumes using multiposture MRI. 多体位MRI评价重力对肝脾体积的影响。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-10 DOI: 10.1007/s12194-024-00870-2

Seiya Nakagawa, Tosiaki Miyati, Naoki Ohno, Yuki Oda, Haruka Kashiwagi, Satoshi Kobayashi

引用次数: 0

Radiomics and dosiomics approaches to estimate lung function after stereotactic body radiation therapy in patients with lung tumors. 放射组学和剂量组学方法评估肺肿瘤患者立体定向放射治疗后的肺功能。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-14 DOI: 10.1007/s12194-024-00877-9

Yoshiro Ieko, Noriyuki Kadoya, Shohei Tanaka, Koyo Kikuchi, Takaya Yamamoto, Hisanori Ariga, Keiichi Jingu

{"title":"Radiomics and dosiomics approaches to estimate lung function after stereotactic body radiation therapy in patients with lung tumors.","authors":"Yoshiro Ieko, Noriyuki Kadoya, Shohei Tanaka, Koyo Kikuchi, Takaya Yamamoto, Hisanori Ariga, Keiichi Jingu","doi":"10.1007/s12194-024-00877-9","DOIUrl":"10.1007/s12194-024-00877-9","url":null,"abstract":"Lung function assessment is essential for determining the optimal treatment strategy for radiation therapy in patients with lung tumors. This study aimed to develop radiomics and dosiomics approaches to estimate pulmonary function test (PFT) results in post-stereotactic body radiation therapy (SBRT). Sixty-four patients with lung tumors who underwent SBRT were included. Models were created to estimate the PFT results at 0-6 months (Cohort 1) and 6-24 months (Cohort 2) after SBRT. Radiomics and dosiomics features were extracted from the computed tomography (CT) images and dose distributions, respectively. To estimate the PFT results, Models A (dose-volume histogram [DVH] + radiomics features) and B (DVH + radiomics + dosiomics features) were created. In the PFT results, the forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) were estimated using each model, and the ratio of FEV1 to FVC (FEV1/FVC) was calculated. The Pearson's correlation coefficient (Pearson r) and area under the curve (AUC) for FEV1/FVC (< 70%) were calculated. The models were evaluated by comparing them with the conventional calculation formulae (Conventional). The Pearson r (FEV1/FVC) values were 0.30, 0.64, and 0.69 for Conventional and Models A and B (Cohort 2), respectively, and the AUC (FEV1/FVC < 70%) values were 0.63, 0.80, and 0.78, respectively. This study demonstrates the possibility of estimating lung function after SBRT using radiomics and dosiomics features based on planning CT images and dose distributions.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"238-248"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of image shoot timing for cerebral veins 3D-digital subtraction angiography by interventional angiography systems. 通过介入血管造影系统优化脑静脉三维数字减影血管造影的图像拍摄时机。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-03-01 Epub Date: 2024-10-29 DOI: 10.1007/s12194-024-00852-4

Kazuya Saeki, Takayuki Tamura, Shingo Kouno, Eiji Nishimaru, Masao Kiguchi, Takafumi Mitsuhara, Kazuo Awai

{"title":"Optimization of image shoot timing for cerebral veins 3D-digital subtraction angiography by interventional angiography systems.","authors":"Kazuya Saeki, Takayuki Tamura, Shingo Kouno, Eiji Nishimaru, Masao Kiguchi, Takafumi Mitsuhara, Kazuo Awai","doi":"10.1007/s12194-024-00852-4","DOIUrl":"10.1007/s12194-024-00852-4","url":null,"abstract":"3D-digital subtraction angiography (3D-DSA) is essential for understanding the anatomical structure of cerebral veins, crucial in brain tumor surgery. 3D-DSA produces three-dimensional images of veins by adjusting the X-ray delay time after contrast agent injection, but the delineation of veins varies with the delay in X-ray timing. Our study aimed to refine the delay time using time-enhancement curve (TEC) analysis from 2D-DSA conducted before 3D-DSA imaging. We retrospectively reviewed 26 meningioma patients who underwent cerebral angiography from March 2020 to August 2021. Using 2D-DSA, we analyzed arterial and venous TECs to determine the contrast agent's peak time and estimated the optimal imaging timing. Cases performed near this optimal time were in Group A, and others in Group B, with cerebral venous pixel values compared between them. TEC analysis identified peak times: internal carotid artery: 2.8 ± 0.7 s, middle cerebral artery (M4): 4.1 ± 0.9 s, superior sagittal sinus: 8.3 ± 1.1 s, sigmoid sinus: 9.5 ± 1.3 s, and venous structures near tumors: 7.3 ± 1.0 s. We observed several veins peaking immediately after arterial contrast passage, suggesting the optimal X-ray delay should incorporate the arterial contrast agent's transit time. Statistical analysis revealed that Group A, with imaging timed to reflect the contrast agent transit time, demonstrated significantly better contrast effects than Group B. The X-ray delay time for 3D-DSA imaging of cerebral veins can be optimized in angiography systems by incorporating the contrast agent transit time, calculated from TEC analysis of cerebral 2D-DSA images.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"36-46"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visualization of X-ray fields, overlaps, and over-beaming on surface of the head in spiral computed tomography using computer-aided design-based X-ray beam modeling. 利用基于计算机辅助设计的 X 射线束建模，对螺旋计算机断层扫描中头部表面的 X 射线场、重叠和过束进行可视化。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-03-01 Epub Date: 2024-10-17 DOI: 10.1007/s12194-024-00849-z

Atsushi Fukuda, Nao Ichikawa, Takuma Hayashi, Ayaka Hirosawa, Kosuke Matsubara

引用次数: 0

Peripheral dose assessment in radiation therapy using photon beams: experimental results with optically stimulated luminescence dosimeter. 光子束放射治疗的外周剂量评估：光激发发光剂量计的实验结果。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-25 DOI: 10.1007/s12194-025-00883-5

Y Retna Ponmalar, Ravikumar Manickam, Henry Finlay Godson, Kadirampatti Mani Ganesh, Sathiyan Saminathan, Varatharaj Chandraraj, Arun Raman

{"title":"Peripheral dose assessment in radiation therapy using photon beams: experimental results with optically stimulated luminescence dosimeter.","authors":"Y Retna Ponmalar, Ravikumar Manickam, Henry Finlay Godson, Kadirampatti Mani Ganesh, Sathiyan Saminathan, Varatharaj Chandraraj, Arun Raman","doi":"10.1007/s12194-025-00883-5","DOIUrl":"10.1007/s12194-025-00883-5","url":null,"abstract":"The estimation of peripheral dose (PD) is vital in cancer patients with long life expectancy. Assessment of PD to radiosensitive organs is important to determine the possible risk of late effects. An attempt has been made to assess the peripheral dose using optically stimulated luminescence dosimeter (OSLD) with megavoltage photon beams as a function of field size, depth, energy, and distance from the field edge. The PD measurements were carried out at 13 locations starting from 1.5 cm to 20.8 cm from radiation field edge for three different field sizes at three different depths with 6 and 18 MV photon beams. In addition, the measurements were carried out to analyze the response in PD due to the presence of wedge. The %PD decreases gradually with an increase in distance from the radiation field edge. The %PD at surface for 10 × 10cm2 with 6MV photon beams was 6.77 ± 0.32% and 1.0 ± 0.04% at 1.5 cm and 20.8 cm away from field edge. For 20 × 20 cm2 field, %PD was found to be much higher at surface than at 5 cm depth for all distances from field edge. This study demonstrates the suitability of OSLD for PD assessment in megavoltage photon beams. The PD increases as field size increases, primarily due to greater amount of out-of-field scatter generated by larger surface area of the collimator defining the larger field size. An enhancement in PD was observed with wedge when the thick end was oriented towards the OSLDs. This study assessed PD that would be a risk factor of the normal tissue complication and secondary cancer induction.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"275-286"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143041542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of dose distributions and respiratory motion tolerance for layer-stacking conformal carbon-ion radiotherapy. 评估层叠适形碳离子放射治疗的剂量分布和呼吸运动耐受性。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-03-01 Epub Date: 2024-11-14 DOI: 10.1007/s12194-024-00847-1

Yuki Hasebe, Mutsumi Tashiro, Hiroshi Sakurai

{"title":"Evaluation of dose distributions and respiratory motion tolerance for layer-stacking conformal carbon-ion radiotherapy.","authors":"Yuki Hasebe, Mutsumi Tashiro, Hiroshi Sakurai","doi":"10.1007/s12194-024-00847-1","DOIUrl":"10.1007/s12194-024-00847-1","url":null,"abstract":"While layer-stacking irradiation provides a conformal dose distribution, it is vulnerable to respiratory motion. Considering that the motion tolerance has not yet been demonstrated, this study aimed to determine the tolerance level for the amount of target motion. Dose distributions considering motion were simulated for a numerical water phantom using in-house software. Comparisons with measured and simulated physical dose distributions confirmed the validity of the simulation, with gamma analysis showing almost 90% or greater agreement under all conditions with a criterion of 3%/3 mm. The variation in physical dose from static conditions followed a similar trend. Based on the evaluation of the simulated clinical dose uniformity, motion tolerance was derived. The acceptable motion amounts in the lateral direction were 11 mm in respiratory-ungated condition and at least 20 mm with 30% lateral gating at 4 Gy (RBE). In the longitudinal (beam upstream) direction, the acceptable target motion amounts were 3 mm without gating and 6 mm with gating. These results employed worst-case scenarios considering multiple respiratory cycles. In both lateral and longitudinal directions, the motion amounts of 3 mm for non-gating and 5 mm for gating were acceptable. The acceptable target motion amounts improved by 1-9 mm with gating and increased prescribed doses. The dose uniformity and motion tolerance under multiple conditions, although based on a simple system, may be useful for treatment involving target motion in layer-stacking irradiation.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"3-16"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142630070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Standardization of radiation therapy quality control system through mutual quality control based on failure mode and effects analysis. 通过基于失效模式和效应分析的相互质量控制，实现放射治疗质量控制系统的标准化。

IF 1.7

Radiological Physics and Technology Pub Date : 2025-03-01 Epub Date: 2024-11-18 DOI: 10.1007/s12194-024-00857-z

Yuki Tanimoto, Masataka Oita, Kazunobu Koshi, Kiyoshi Ishiwaki, Futoshi Hiramatsu, Toshihisa Sasaki, Hiroki Ise, Takashi Miyagawa, Takeshi Maeda, Shinsuke Okahira, Takashi Hamaguchi, Tatsuya Kawaguchi, Norihiro Funada, Shuhei Yamamoto, Akira Hiroshige, Yuki Mukai, Shohei Yoshida, Yoshiki Fujita, Atsuki Nakahira, Hirofumi Honda

{"title":"Standardization of radiation therapy quality control system through mutual quality control based on failure mode and effects analysis.","authors":"Yuki Tanimoto, Masataka Oita, Kazunobu Koshi, Kiyoshi Ishiwaki, Futoshi Hiramatsu, Toshihisa Sasaki, Hiroki Ise, Takashi Miyagawa, Takeshi Maeda, Shinsuke Okahira, Takashi Hamaguchi, Tatsuya Kawaguchi, Norihiro Funada, Shuhei Yamamoto, Akira Hiroshige, Yuki Mukai, Shohei Yoshida, Yoshiki Fujita, Atsuki Nakahira, Hirofumi Honda","doi":"10.1007/s12194-024-00857-z","DOIUrl":"10.1007/s12194-024-00857-z","url":null,"abstract":"The advancement of irradiation technology has increased the demand for quality control of radiation therapy equipment. Consequently, the number of quality control items and required personnel have also increased. However, differences in the proportion of qualified personnel to irradiation techniques have caused bias in quality control systems among institutions. To standardize the quality across institutions, researchers should conduct mutual quality control by analyzing the quality control data of one institution at another institution and comparing the results with those of their own institutions. This study uses failure mode and effects analysis (FMEA) to identify potential risks in 12 radiation therapy institutions, compares the results before and after implementation of mutual quality control, and examines the utility of mutual quality control in risk reduction. Furthermore, a cost-effectiveness factor is introduced into FMEA to evaluate the utility of mutual quality control.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"78-85"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0